Theory and implementation of depth photography

Traditional imaging methods are subject to limitations on information acquisition, which brings about the deficiency of image quality. Therefore, a depth photography model was proposed here. This model contains a depth matrix, a decomposition function, a defocus operator, and an adaptive regularization term. The depth matrix could be estimated using the binocular stereo vision method, the structured light approach, or the time-of-flight algorithm. The decomposition function was used to segment the image into pieces according to the distinct depth values. The defocus operator was calculated through a defocus-from-depth method. The adaptive regularization term reduces the staircase effect and enhances image smoothness. Local standard deviations and local average gradients were used to evaluate the effectiveness of depth photography model. Experimental results demonstrate that the proposed model is effective.